Field of the Invention
The invention relates to a method for determining spraying parameters that are suitable as input values for a paint spraying unit that can electrostatically charge a liquid paint.
If a specific painting result of an electrostatically based painting unit is prescribed, that is to say is targeted, the associated physical spraying parameters such as paint volume, directing-air rate, control-air rate or high voltage must be determined with the aid of a paint thickness distribution to be targeted. This is performed in practice essentially on the basis of empirical experience on the part of the user of painting units. Although the average paint thickness distribution can be determined with the aid of the average paint throughput per surface under consideration, there is, however, no method for determining the spraying parameters corresponding to an exact paint thickness distribution.
It is accordingly an object of the invention to provide a method for determining spraying parameters for a paint spraying unit that overcomes the disadvantages of the prior art methods of this general type.
With the foregoing and other objects in view there is provided, in accordance with the invention, a method for determining spraying parameters suitable as input values for a paint spraying unit that can electrostatically charge liquid paint. The method includes using at least one artificial neural network to determine the spraying parameters, and the artificial neural network has an output available for each of the spraying parameters. A suitable number of real measured values are fed to the at least one artificial neural network as input values, initially in a learning phase, the real measured values contain associated real spraying parameters in addition to a paint thickness distribution in a form of discrete values. The input values are fed to the at least one artificial neural network in an application phase, the input values being a result of an analysis of the paint thickness distribution of a prescribed spraying result.
It is the object of the invention to specify a method with the aid of which, in the case of the prescription of a specific painting result, that is to say a paint thickness distribution, the input values thereby required for the paint spraying unit, that is to say the physical spraying parameters, can be determined.
In this method, the spraying parameters to be set are determined from a desired spray pattern which is to be attained, with the aid of artificial neural networks which are trained with the aid of real measured data.
In accordance with an added feature of the invention, there is the step of determining the real measured values, to be input in the learning phase, at least partly by a mathematical model of the paint spraying unit.
In accordance with an additional feature of the invention, for each of the spraying parameters to be determined, there is the step of using one artificial neural network having an output value bearing a fixed relationship to the spraying parameter to be determined.
In accordance with another feature of the invention, the at least one neural network has a plurality of outputs which respectively correspond to one of the spraying parameters to be determined.
In accordance with a concomitant feature of the invention, there is the step of using a multilayer perceptron trained with an aid of a backpropagation method as the at least one artificial neural network.
Other features which are considered as characteristic for the invention are set forth in the appended claims.
Although the invention is illustrated and described herein as embodied in a method for determining spraying parameters for a paint spraying unit, it is nevertheless not intended to be limited to the details shown, since various modifications and structural changes may be made therein without departing from the spirit of the invention and within the scope and range of equivalents of the claims.
The construction and method of operation of the invention, however, together with additional objects and advantages thereof will be best understood from the following description of specific embodiments when read in connection with the accompanying drawings.